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---
library_name: transformers
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: windowz_020625
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# windowz_020625
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.9891
- F1: 0.9871
- Iou: 0.9786
- Contour Dice: 0.9923
- Per Class Metrics: {0: {'f1': 0.99741, 'iou': 0.99483, 'accuracy': 0.99611, 'contour_dice': 0.99741}, 1: {'f1': 0.97774, 'iou': 0.95645, 'accuracy': 0.98909, 'contour_dice': 0.97774}, 2: {'f1': 0.41176, 'iou': 0.25926, 'accuracy': 0.99298, 'contour_dice': 0.41176}}
- Loss: 0.0301
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | | Dice | Class Metrics | Validation Loss |
|:-------------:|:-----:|:-----:|:------:|:------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------:|
| 0.4629 | 5.0 | 12815 | 0.9623 | 0.9725 | {0: {'f1': 0.99106, 'iou': 0.98228, 'accuracy': 0.98651, 'contour_dice': 0.99106}, 1: {'f1': 0.96086, 'iou': 0.92467, 'accuracy': 0.9813, 'contour_dice': 0.96086}, 2: {'f1': 0.51615, 'iou': 0.34784, 'accuracy': 0.99358, 'contour_dice': 0.51615}} | 0.0652 |
| 0.4439 | 10.0 | 25630 | 0.9786 | 0.9923 | {0: {'f1': 0.99741, 'iou': 0.99483, 'accuracy': 0.99611, 'contour_dice': 0.99741}, 1: {'f1': 0.97774, 'iou': 0.95645, 'accuracy': 0.98909, 'contour_dice': 0.97774}, 2: {'f1': 0.41176, 'iou': 0.25926, 'accuracy': 0.99298, 'contour_dice': 0.41176}} | 0.0301 |
| 0.4077 | 15.0 | 38445 | 0.2129 | 0.4508 | {0: {'f1': 0.31665, 'iou': 0.18811, 'accuracy': 0.39105, 'contour_dice': 0.31665}, 1: {'f1': 0.43873, 'iou': 0.28101, 'accuracy': 0.38547, 'contour_dice': 0.43873}, 2: {'f1': 0.60057, 'iou': 0.42915, 'accuracy': 0.99441, 'contour_dice': 0.60057}} | 0.8030 |
| 0.4099 | 20.0 | 51260 | 0.9796 | 0.9892 | {0: {'f1': 0.9964, 'iou': 0.99283, 'accuracy': 0.9946, 'contour_dice': 0.9964}, 1: {'f1': 0.97792, 'iou': 0.9568, 'accuracy': 0.98934, 'contour_dice': 0.97792}, 2: {'f1': 0.67584, 'iou': 0.51039, 'accuracy': 0.99419, 'contour_dice': 0.67584}} | 0.0441 |
| 0.3872 | 25.0 | 64075 | 0.9799 | 0.9882 | {0: {'f1': 0.99605, 'iou': 0.99212, 'accuracy': 0.99407, 'contour_dice': 0.99605}, 1: {'f1': 0.9783, 'iou': 0.95752, 'accuracy': 0.98953, 'contour_dice': 0.9783}, 2: {'f1': 0.73739, 'iou': 0.58402, 'accuracy': 0.99521, 'contour_dice': 0.73739}} | 0.0314 |
### Framework versions
- Transformers 4.45.0
- Pytorch 2.5.1+cu124
- Datasets 2.21.0
- Tokenizers 0.20.3
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